ReviewFrontiers in plant science2025
Cloud-edge-device collaborative computing in smart agriculture: architectures, applications, and future perspectives.
Review in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- An Embedded Multi-Sensor IoT Platform for Agricultural Monitoring Using Meshtastic-Based LoRa Communication and Cloud Microservices.Sensors (Basel, Switzerland) · 2026Article
- A Review of Fault Diagnosis and Intelligent Operations and Maintenance for Agricultural Machinery: Fault Mechanisms, Key Technologies, and Practical Recommendations.Sensors (Basel, Switzerland) · 2026Review
- Generative artificial intelligence in animal genomics for smart agriculture: Applications, challenges, and future prospects.Veterinary and animal science · 2026Review
- Deterministic Edge-Controlled Precision Fertigation System with Spatial Task Scheduling and Hardware-Software Safety Interlock.Sensors (Basel, Switzerland) · 2026Article
- Real-Time Beef Cattle Body Condition Scoring Using EdgeBCS-YOLO: A Lightweight Framework for Edge Deployment.Animals : an open access journal from MDPI · 2026Article
- AI-enabled smart farming framework for sustainable date palm cultivation in arid regions using machine learning and IoT integration.Scientific reports · 2026Article
- IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smart agriculture is rapidly evolving in response to growing global demands for food security and sustainable resource management. Cloud-edge-device collaborative computing has emerged as a transformative paradigm, addressing the limitations of traditional centralized architectures by enabling distributed intelligence, real-time processing, and adaptive decision-making. This review provides a comprehensive overview of the architectures, technical characteristics, and application scenarios of cloud-edge-device collaboration in agriculture. Key domains covered include environmental monitoring, intelligent irrigation, UAV-machinery coordination, livestock health management, and pest and disease control. Major challenges such as device heterogeneity, data consistency, resource constraints, and privacy concerns are identified and discussed. Furthermore, six critical research directions are outlined, including intelligent scheduling algorithms, lightweight edge AI, hierarchical data fusion, federated learning, interoperability frameworks, and digital twin technologies. This review aims to serve as a practical reference and theoretical foundation for advancing the design and implementation of next-generation smart agriculture systems.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.